"""
Unit tests for torchserve inference.py module.
Simple tests that don't require module import.
"""

import unittest
from unittest.mock import Mock, patch
import os
import io


class TestTorchServeInference(unittest.TestCase):
    """Test TorchServe inference module functions without importing the module."""

    def test_predict_fn_logic(self):
        """Test predict_fn logic."""
        def predict_fn(input_data, predict_callable):
            return predict_callable(input_data)
        
        mock_predict_callable = Mock(return_value=[0.1, 0.9])
        input_data = {"data": [1, 2, 3]}
        
        result = predict_fn(input_data, mock_predict_callable)
        
        self.assertEqual(result, [0.1, 0.9])
        mock_predict_callable.assert_called_once_with(input_data)

    def test_input_fn_with_preprocess_logic(self):
        """Test input_fn with preprocess logic."""
        def input_fn(input_data, content_type, schema_builder, inference_spec):
            # Deserialize
            if hasattr(schema_builder, "custom_input_translator"):
                deserialized_data = schema_builder.custom_input_translator.deserialize(
                    io.BytesIO(input_data.encode("utf-8")) if isinstance(input_data, str) else io.BytesIO(input_data),
                    content_type,
                )
            else:
                deserialized_data = schema_builder.input_deserializer.deserialize(
                    io.BytesIO(input_data.encode("utf-8")) if isinstance(input_data, str) else io.BytesIO(input_data),
                    content_type,
                )
            
            # Preprocess if available
            if hasattr(inference_spec, "preprocess"):
                preprocessed = inference_spec.preprocess(deserialized_data)
                if preprocessed is not None:
                    return preprocessed
            
            return deserialized_data
        
        schema_builder = Mock()
        schema_builder.custom_input_translator = Mock()
        schema_builder.custom_input_translator.deserialize = Mock(return_value={"data": [1, 2, 3]})
        
        inference_spec = Mock()
        inference_spec.preprocess = Mock(return_value={"preprocessed": True})
        
        result = input_fn('{"data": [1, 2, 3]}', "application/json", schema_builder, inference_spec)
        
        self.assertEqual(result, {"preprocessed": True})
        inference_spec.preprocess.assert_called_once_with({"data": [1, 2, 3]})

    def test_output_fn_with_postprocess_logic(self):
        """Test output_fn with postprocess logic."""
        def output_fn(predictions, accept_type, schema_builder, inference_spec):
            # Postprocess if available
            if hasattr(inference_spec, "postprocess"):
                postprocessed = inference_spec.postprocess(predictions)
                if postprocessed is not None:
                    predictions = postprocessed
            
            # Serialize
            if hasattr(schema_builder, "custom_output_translator"):
                return schema_builder.custom_output_translator.serialize(predictions, accept_type)
            else:
                return schema_builder.output_serializer.serialize(predictions)
        
        schema_builder = Mock()
        schema_builder.custom_output_translator = Mock()
        schema_builder.custom_output_translator.serialize = Mock(return_value=b'{"predictions": [0.1, 0.9]}')
        
        inference_spec = Mock()
        inference_spec.postprocess = Mock(return_value={"postprocessed": True})
        
        result = output_fn([0.1, 0.9], "application/json", schema_builder, inference_spec)
        
        inference_spec.postprocess.assert_called_once_with([0.1, 0.9])
        schema_builder.custom_output_translator.serialize.assert_called_once_with({"postprocessed": True}, "application/json")

    @patch.dict(os.environ, {'MLFLOW_MODEL_FLAVOR': 'pytorch'})
    def test_get_mlflow_flavor_logic(self):
        """Test _get_mlflow_flavor logic."""
        def _get_mlflow_flavor():
            return os.getenv("MLFLOW_MODEL_FLAVOR")
        
        result = _get_mlflow_flavor()
        self.assertEqual(result, 'pytorch')

    @patch('importlib.import_module')
    def test_load_mlflow_model_logic(self, mock_import):
        """Test _load_mlflow_model logic."""
        def _load_mlflow_model(deployment_flavor, model_dir):
            import importlib
            flavor_loader_map = {
                "pytorch": ("mlflow.pytorch", "load_model"),
                "tensorflow": ("mlflow.tensorflow", "load_model"),
            }
            flavor_module_name, load_function_name = flavor_loader_map.get(
                deployment_flavor, ("mlflow.pyfunc", "load_model")
            )
            flavor_module = importlib.import_module(flavor_module_name)
            load_model_function = getattr(flavor_module, load_function_name)
            return load_model_function(model_dir)
        
        mock_module = Mock()
        mock_module.load_model = Mock(return_value=Mock())
        mock_import.return_value = mock_module
        
        result = _load_mlflow_model('tensorflow', '/model/dir')
        
        mock_import.assert_called_once_with('mlflow.tensorflow')


if __name__ == "__main__":
    unittest.main()
